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Mass communications and advances in communications technology pose fundamental challenges to the traditional conduct of diplomacy by reducing hierarchy, promoting transparency, crowding out secrecy, mobilizing global social movements, and increasing the importance of public diplomacy in international relations. But the primary source of change, the force that acts as a common denominator and accelerates other changes, is communications and information technology (CIT). Where nations were once connected through foreign ministries and traders, they are now linked to millions of individuals by fibre optics, satellite, wireless, and cable in a complex network without central control. These trends have resulted in considerable speculation about the future of diplomacy.

Discusses the evolving symbiosis between Africa and China and specifies its likely implications. Among the specific topics tackled here are China's interest in African oil, military and security relations, the influx and goals of Chinese aid to sub-Saharan Africa, human rights issues, and China's overall strategy in the region

Lithium has broad applications in several emerging industries and fields, including high energy batteries, energy storage, aerospace, and controlled nuclear reactions. Currently, the discrepancy between the supply and demand for lithium resources increases, and its distribution is uneven. Within the framework of the \"Belt and Road\" Initiative, the lithium trade pattern evolves constantly. However, the trade pattern of lithium in the nations along the \"Belt and Road\" is likely to face substantial repercussions in modern world of unilateral protectionism and geopolitical conflicts. Taking the social network analysis approach as a tool, this study first examines the characteristics of the lithium trade network structure as it has evolved over the years in the Belt and Road countries, from 2000 to 2022. Additionally, this study uses the quadratic assignment problem approach to analyze the factors influencing the evolution of the lithium trade network. The study shows that: (1) The spatial patterns of import and export trade network of lithium in countries along the route has a certain path dependence. And the market is mainly concentrated in East Asia, Central and Eastern Europe, South America and Southeast Asia. (2) The network density of the countries along the route has increased year after year, but it remains low. And the fluctuation of the network’s reciprocity has increased, with a huge magnitude of variation. The number of core countries in the network has decreased over time, but the core-periphery structure has stayed largely steady. China, Chile, and South Korea are the network’s main node countries. (3) Regarding the influencing factors, the differences in economic and technological development between these countries have a beneficial impact on the formation of lithium trade; whether or not regional trade agreements have been signed, the differences about average tax rates for mineral products, bordering countries, and similar languages and cultures are all conducive to the establishment of close trade links. The contribution of this essay is of paramount importance for understanding different countries’ role along the Belt and Road in the lithium trade network pattern, and promoting regional trade cooperation.

Future climate change will impose increased variability on food production and food trading networks. However, the effect of climate variability and sudden shocks on resource availability through trade and its subsequent effect on population growth is largely unknown. Here we study the effect of resource variability and network topology on access to resources and population growth, using a model of population growth limited by resource availability in a trading network. Resources are redistributed in the network based on supply and the distance between nodes (i.e. cities or countries). Resources at nodes vary over time with wave parameters that mimic changes in biomass production arising from known climate variability. Random perturbations to resources are applied to study resilience of individual nodes and the system as a whole. The model demonstrates that redistribution of resources increases the maximum population that can be supported (carrying capacity) by the network. Fluctuations in carrying capacity depend on the amplitude and frequency of resource variability: fluctuations become larger for increasing amplitude and decreasing frequency. Our study shows that topology is the key factor determining the carrying capacity of a node. In larger networks the carrying capacity increases and the distribution of resources in the network becomes more equal. The most central nodes achieve a higher carrying capacity than nodes with a lower centrality. Moreover, central nodes are less susceptible to long-term resource variability and shocks. These insights can be used to understand how worldwide equitable access to resources can be maintained under increasing climate variability.

Background Classifying and mapping vegetation are crucial tasks in environmental science and natural resource management. However, these tasks are difficult because conventional methods such as field surveys are highly labor-intensive. Identification of target objects from visual data using computer techniques is one of the most promising techniques to reduce the costs and labor for vegetation mapping. Although deep learning and convolutional neural networks (CNNs) have become a new solution for image recognition and classification recently, in general, detection of ambiguous objects such as vegetation is still difficult. In this study, we investigated the effectiveness of adopting the chopped picture method, a recently described protocol for CNNs, and evaluated the efficiency of CNN for plant community detection from Google Earth images. Results We selected bamboo forests as the target and obtained Google Earth images from three regions in Japan. By applying CNN, the best trained model correctly detected over 90% of the targets. Our results showed that the identification accuracy of CNN is higher than that of conventional machine learning methods. Conclusions Our results demonstrated that CNN and the chopped picture method are potentially powerful tools for high-accuracy automated detection and mapping of vegetation.

With the rising geopolitical tensions, predicting future trade partners has become a critical topic for the global community. Liquefied natural gas (LNG), recognized as the cleanest burning hydrocarbon, plays a significant role in the transition to a cleaner energy future. As international trade in LNG becomes increasingly volatile, it is essential to assist governments in identifying potential trade partners and analyzing the trade network. Traditionally, forecasts of future mineral and energy resource trade networks have relied on similarity indicators (e.g., CN, AA). This study employs complex network theory to illustrate the characteristics of nodes and edges, as well as the evolution of global LNG trade networks from 2001 to 2020. Utilizing node and edge data from these networks, this research applies machine learning algorithms to predict future links based on local and global similarity-based indices (e.g., CN, JA, PA). The findings indicate that random forest and decision tree algorithms, when used with local similarity-based indices, demonstrate strong predictive performance. The reliability of these algorithms is validated through the Receiver Operating Characteristic Curve (ROC). Additionally, a graph attention network model is developed to predict potential links using edge and motif data. The results indicate robust predictive performance. This study demonstrates that machine learning algorithms—specifically random forest and decision tree—outperform in predicting links within the global LNG trade network based on local information proximity, while the graph attention network, a deep learning model, exhibits stable optimization and effective feature learning. These findings suggest that machine learning approaches hold significant promise for mineral trade network analysis.

Smart farming (SF) applications rely on robust and accurate computer vision systems. An important computer vision task in agriculture is semantic segmentation, which aims to classify each pixel of an image and can be used for selective weed removal. State-of-the-art implementations use convolutional neural networks (CNN) that are trained on large image datasets. In agriculture, publicly available RGB image datasets are scarce and often lack detailed ground-truth information. In contrast to agriculture, other research areas feature RGB-D datasets that combine color (RGB) with additional distance (D) information. Such results show that including distance as an additional modality can improve model performance further. Therefore, we introduce WE3DS as the first RGB-D image dataset for multi-class plant species semantic segmentation in crop farming. It contains 2568 RGB-D images (color image and distance map) and corresponding hand-annotated ground-truth masks. Images were taken under natural light conditions using an RGB-D sensor consisting of two RGB cameras in a stereo setup. Further, we provide a benchmark for RGB-D semantic segmentation on the WE3DS dataset and compare it with a solely RGB-based model. Our trained models achieve up to 70.7% mean Intersection over Union (mIoU) for discriminating between soil, seven crop species, and ten weed species. Finally, our work confirms the finding that additional distance information improves segmentation quality.

As the field of international business has matured, there have been shifts in the core unit of analysis. First, there was analysis at country level, using national statistics on trade and foreign direct investment (FDI). Next, the focus shifted to the multinational enterprise (MNE) and the parent's firm specific advantages (FSAs). Eventually the MNE was analysed as a network and the subsidiary became a unit of analysis. We untangle the last fifty years of international business theory using a classification by these three units of analysis. This is the country-specific advantage (CSA) and firm-specific advantage (FSA) matrix. Will this integrative framework continue to be useful in the future? We demonstrate that this is likely as the CSA/FSA matrix permits integration of potentially useful alternative units of analysis, including the broad region of the triad. Looking forward, we develop a new framework, visualized in two matrices, to show how distance really matters and how FSAs function in international business. Key to this are the concepts of compounded distance and resource recombination barriers facing MNEs when operating across national borders.

Chameleons (family Chamaeleonidae) are a distinctive group of reptiles, mainly found in Africa, which have high local endemism and face significant threats from the international wildlife trade. We review the scale and structure of international chameleon trade, with a focus on collection in and exports from Tanzania; a hotspot of chameleon diversity. Analysis used data from the CITES Trade Database 2000–2019, combined with assessment of online trade, and on-the-ground surveys in Tanzania in 2019. Between 2000 and 2019, 1,128,776 live chameleons from 108 species were reported as exported globally, with 193,093 of these (from 32 species) exported by Tanzania. Both global and Tanzanian chameleon exports declined across the study period, driven by decreased trade in generalist genera. Whilst the proportion of captive-bred individuals increased across time for the generalist taxa, the majority of range-restricted taxa in trade remained largely wild-sourced. For Tanzanian exports, 41% of chameleons were from one of the 23 endemic species, and 10 of the 12 Tanzanian endemic species in trade are categorised as threatened with extinction by IUCN. In terms of online trade, of the 42 Tanzanian species assessed, there was evidence of online sale for 83.3% species, and 69% were actively for sale with prices listed. Prices were on average highest for Trioceros species, followed by Kinyongia , Rieppeleon , Rhampholeon , and Chameleo . Field work in Tanzania provided evidence that the historic harvest of endemic chameleon species has been higher than the quantities of these species reported as exported by Tanzania in their annual trade reports to CITES. However, we found no field evidence for trade in 2020 and 2021, in line with Tanzanian regulations that applied a blanket ban on all exports of live wild animals. Literature evidence, however, suggests that illegal trade continued to Europe from seizures of Tanzanian chameleon species in Austria in 2021.

In recent years, artificial intelligence-based solutions have become widespread in various fields and have been observed to produce important solutions to critical problems. In this context, it is aimed to assess and establish a direct correlation between energy production/consumption and establishing sustainable business models by using artificial intelligence models. Thus, artificial intelligence-based models have been developed by using parameters related to global energy consumption, doing business, and critical concepts of the relevant topics. The results show that the proposed artificial intelligent-based models reveal a significant correlation between doing business and energy. The outcome of the study could be used in the determination of country strategies in critical areas such as transportation, infrastructure, and education in the near and far future.